Supercharge Your Analytics and AI with Intel

Table of Contents

1. Introduction
2. The Importance of Data Transformation
3. Intel's Commitment to Data and Analytics
4. Hardware Level Innovations
   • Faster Processors
   • Low Latency Fabrics
   • High-Speed Storage
5. Data Platform Optimization
   • Hadoop and Spark on Intel Hardware
   • Performance and Security Enhancements
6. Accelerating Analytics
   • Hadoop and Spark Based Analytics Deployment
   • Distributed Machine Learning and Deep Learning
7. Delivering a High-Performance Experience
   • Intel Math Kernel Library
   • Optimization Software for SPARC Machine Learning Workloads
   • Storage Acceleration Library for HDFS Erasure Coding Computing
8. Enhancing Security
   • Utilizing Intel Security Instruction Set
   • Encryption for SPARC Shuffle and Spark RPC
9. Achieving Scalability
   • Improving Analytics Application for Online Retailers
   • Scaling LDa Machine Learning for Internet Companies
10. Beyond Traditional Machine Learning
    • Challenges of Integrating Deep Learning
    • Introducing BigDL: A Distributed Deep Learning Framework
11. Benefits of BigDL
    • Feature Parity with Existing Deep Learning Frameworks
    • Integration with Hadoop and Spark
    • Efficient Scale-Out and Resource Management
    • Cost of Ownership Considerations
12. High Performance on Xeon Clusters
    • Utilizing Intel Optimized Math Kernel Library
13. Industry Adoption and Collaborations
    • Cloud Service Providers and Tutorials
    • Use Cases in Manufacturing and Financial Services
    • Collaboration in Telco, Enterprise, Government, and Healthcare Sectors
14. Intel's Comprehensive Technology Portfolio
    • From Ziya, Xeon Phi, FPGA to Nirvana
    • Enhanced Performance at Software, Framework, and Application Levels
15. Conclusion

Article

Introduction

In today's data-driven world, the ability to Gather and analyze massive amounts of data has become paramount. This transformation has led to the need for advanced hardware and software solutions that can process and convert this data into valuable insights and intelligence. Intel, a leading technology company, recognizes this need and has been continuously innovating to provide the best experience for data and analytics. In this article, we will explore Intel's commitment to data transformation, the innovations they have made at the hardware and software level, and their efforts to enhance performance, security, and scalability in the field of analytics.

The Importance of Data Transformation

Data transformation is the process of converting raw data into Meaningful information that can drive decision-making and improve user experience. In today's interconnected world, data is generated by people, their computers, and various connected devices. This data holds immense potential for businesses and organizations, enabling them to gain valuable insights about their customers, processes, and operations. Therefore, the ability to effectively gather, process, and analyze this data is essential for staying competitive and driving innovation.

Intel's Commitment to Data and Analytics

Intel's CEO, Brian Krzanich, has stated that Intel is a data company. This declaration reflects Intel's recognition of the transformative power of data in various industries. With a focus on enabling the best experience for data and analytics, Intel has been driving innovation at multiple levels, including hardware, data platforms, and analytics.

Hardware Level Innovations

At the hardware level, Intel has been at the forefront of silicon innovation, delivering faster processors, low latency fabrics, and high-speed storage. These innovations have significantly improved the performance and efficiency of data processing, enabling faster and more accurate analytics.

Data Platform Optimization

In addition to hardware advancements, Intel has been optimizing popular data platforms like Hadoop and Spark on their hardware. By fine-tuning these platforms, Intel has improved the performance, security, and scalability of analytics processes. This optimization has been especially beneficial for businesses dealing with large-scale data analytics.

Accelerating Analytics

Intel has also been instrumental in accelerating the deployment of Hadoop and Spark-based analytics. Through the implementation of large-scale distributed machine learning and deep learning, Intel has helped businesses derive valuable insights and achieve business value from their data. This acceleration has enabled industries such as e-commerce, manufacturing, and financial services to enhance their analytics capabilities and make more informed decisions.

Delivering a High-Performance Experience

One of the key aspects of the best experience in data and analytics is high performance. Intel has addressed this need by leveraging their Intel Math Kernel Library, which optimizes the performance of SPARC machine learning workloads. This optimization software has been merged into the Apache Upstream and is also available in recent releases through HBase. With this optimization, Intel has achieved significant performance improvements, with customers reporting up to 5.6 times faster performance in their machine learning workloads.

Enhancing Security

Security is another crucial aspect of the data and analytics experience. Intel has focused on providing strong security measures by utilizing their Intel Security Instruction Set, such as AES and I. By implementing these instructions, businesses have been able to enhance the encryption and security of their data during processes like SPARC shuffle and Spark RPC. This has resulted in improved security and performance gains for customers.

Achieving Scalability

Scalability is a critical factor for businesses dealing with large amounts of data. Intel has assisted online retailers and internet companies in improving their analytics applications and achieving ten times and seventy times scalability, respectively. By scaling their machine learning processes, businesses have been able to analyze customer behavior at a more granular level, recommend Relevant products, and increase sales.

Beyond Traditional Machine Learning

While traditional machine learning has proven to be valuable, many businesses are now exploring the potential of deep learning. Deep learning presents unique challenges, particularly in distributed environments and at scale. Intel has addressed these challenges by introducing BigDL, a distributed deep learning framework built on Apache Spark.

Benefits of BigDL

BigDL offers feature parity with existing deep learning frameworks like Caffe, Torch, and others. It seamlessly integrates with Hadoop and Spark, providing a unified analytics platform for businesses. This integration allows for consistent and integrated pipelines, from data ingestion to storage, processing, and both traditional machine learning and deep learning. BigDL also scales out deep learning efficiently, utilizing data parallelism and model parallelism. By keeping deep learning on the same cluster as the existing analytics pipeline, businesses can reduce costs and eliminate the need for separate hardware clusters.

High Performance on Xeon Clusters

Intel has optimized BigDL to deliver high performance on Xeon clusters. By utilizing their Intel optimized Math Kernel Library, Intel has ensured that the framework performs efficiently and achieves optimal performance levels when running on their hardware. This high performance allows businesses to reduce their training cycles and make faster, more accurate predictions.

Industry Adoption and Collaborations

BigDL has seen rapid adoption in the industry. Top-tier cloud service providers like Amazon, Microsoft, and Alibaba have published tutorials on using BigDL in their cloud offerings. The framework has also been successfully implemented in various sectors, including manufacturing, financial services, telecom, enterprise, government, and healthcare. These collaborations have resulted in significant performance improvements and enable organizations to tackle complex business challenges with advanced analytics.

Intel's Comprehensive Technology Portfolio

Intel's commitment to enabling the best experience for data and analytics goes beyond BigDL. With a comprehensive technology portfolio, Intel offers a range of solutions from Ziya, Xeon Phi, FPGA, to Nirvana. At the software level, Intel improves performance through libraries like MKL and DAL. They optimize popular deep learning frameworks on their hardware, such as Cafe, Nyan, TensorFlow, and others. Intel also provides SDKs for developers to focus on real value-added deep learning training and deployment, enhancing productivity and efficiency.

Conclusion

Intel's dedication to data and analytics has made them a leader in the field. By continuously innovating at the hardware and software levels, they have provided businesses with the tools they need to gather, process, and analyze data effectively. With the introduction of BigDL, Intel has addressed the challenges of incorporating deep learning into existing analytics pipelines. This framework offers feature parity, scalability, efficient resource management, and cost-effectiveness. Intel's comprehensive technology portfolio further strengthens their position as a provider of comprehensive AI and analytics solutions. As the demand for data-driven insights continues to grow, Intel remains committed to democratizing technologies for AI and analytics and building a better future in collaboration with their customers.